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Photonic Sensors

, Volume 8, Issue 3, pp 220–227 | Cite as

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

  • Baocheng Wang
  • Dandan Qu
  • Qing Tian
  • Liping Pang
Open Access
Regular
  • 63 Downloads

Abstract

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system (OFPS) is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform (MT) is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation (BP) neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Keywords

Linear scale OFPS MT BP neural network spectral characteristics 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61571014 and 61601006); Beijing Nature Science Foundation (Grant No. 4172017); General Project of Science and Technology Program of Beijing Education Commission (Grant No. KM201610009004).

References

  1. [1]
    Z. Z. Qiu, T. Zheng, H. Q. Qu, and L. P. Pang, “A new method based on CFAR and DE for OFPS,” Photonic Sensors, 2016, 6(3): 261–267.ADSCrossRefGoogle Scholar
  2. [2]
    W. Liang, L. L. Lu, and L. B. Zhang, “Coupling relations and early-warning for ‘equipment chain’ in long-distance pipeline,” Mechanical Systems and Signal Processing, 2013, 41(1–2): 335–347.ADSCrossRefGoogle Scholar
  3. [3]
    F. K. Bi, C. Feng, H. Q. Qu, and T. Zheng, “Harmful intrusion detection algorithm of optical fiber pre-warning system based on correlation of orthogonal polarization signals,” Photonic Sensors, 2017, 7(3): 226–233.ADSCrossRefGoogle Scholar
  4. [4]
    K. Liu, T. J. Chai, T. G. Liu, J. F. Jiang, Q. N. Chen, L. Pan, et al. , “Multi-area optical perimeter security system with quick invasion judgement algorithm,” Journal of Optoelectronics Laser, 2015, 26(2): 288–294.Google Scholar
  5. [5]
    L. J. Hang, C. F. He, B. Wu, D. S. Cai, and Y. R. Song, “Research on novel distributed optical fiber pipeline leakage detection technology and location method,” Acta Optica Sinica, 2008, 28(1): 123–127.CrossRefGoogle Scholar
  6. [6]
    C. H. Zhu, Y. Z. Qu, and J. P. Wang, “The vibration signal recognition of optical fiber perimeter based on time-frequency features,” Opto-Electronic Engineering, 2014, 41(1): 16–22.Google Scholar
  7. [7]
    L. Wang, Y. B. Guo, T. G. Sun, J. Y. Huo, and L. Zhang, “Signal recognition of the optical fiber vibration sensor based on two-level feature extraction,” in Proceeding of IEEE 8th International Congress on Image and Signal, Shenyang, China, 2015, pp: 1484–1488.Google Scholar
  8. [8]
    S. S. Mahmoud, Y. Visagathilagar, and J. Katsifolis, “Real-time distributed fiber optic sensor for security systems: performance, event classification and nuisance mitigation,” Photonic Sensors, 2012, 2(3): 225–236.ADSCrossRefGoogle Scholar
  9. [9]
    Z. Y. Wang, Z. Q. Pan, Q. Ye, H. W. Cai, R. H. Qu, and Z. J. Fang, “Fast pattern recognition based on frequency spectrum analysis used for intrusion alarming in optic fiber fence,” Chinese Journal of Lasers, 2015, 42(4): 1–6.Google Scholar
  10. [10]
    H. F. Li, X. D. Yin, J. Z. Liu, C. Z. Zhang, and Y. Chen, “Intrusion signal recognition basing on optical fiber Bragg grating vibration sensor,” Optical Communication Technology, 2012, 2: 12–14.Google Scholar
  11. [11]
    H. J. Wu, S. K. Xiao, X. Y. Li, Z. N. Wang, J. W. Xu, and Y. J. Rao, “Separation and determination of the disturbing signals in phase-sensitive optical time domain reflectometry (Φ-OTDR),” Journal of Lightwave Technology, 2015, 33(15): 3156–3162.ADSCrossRefGoogle Scholar
  12. [12]
    R. Sun and W. J. Zeng, “Secure and robust image hashing via compressive sensing,” Multedia Tools and Applications, 2014, 70(3): 1651–1665.CrossRefGoogle Scholar
  13. [13]
    B. Xiao, J. F. Ma, and J. T. Cui, “Combined blur, translation, scale and rotation invariant image recognition by Radon and pseudo-Fourier-Mellin transforms,” Patern Recogntion, 2012, 45(1): 314–321.CrossRefzbMATHGoogle Scholar
  14. [14]
    X. C. Dai and Q. Xie, “Research on image matching algorithm based on Fourier-Mellin transform,” Infrared Technology, 2016, 38(10): 860–863.Google Scholar
  15. [15]
    S. W. Zhang, S. C. Zhen, X. L. Zhao, and S. T. Zhao, “Recognition method of radar target using range profile,” Systems Engineering and Electronics, 2001, 23(11): 48–51.Google Scholar
  16. [16]
    J. H. Cheng, G. Gao, W. X. Ding, X. S. Ku, and J. X. Sun, “An improved scheme for parameter estimation of G° distribution model in high-resolution SAR images,” Progress in Electromagnetics Research, 2013, 134: 23–46.CrossRefGoogle Scholar
  17. [17]
    J. Yang, T. K. Sarkar, and P. Antonik, “Applying the Fourier-modified Mellin transform (FMMT) to Doppler-istorted waveforms,” Digital Singal Processing, 2007, 17(6): 1030–1039.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Baocheng Wang
    • 1
  • Dandan Qu
    • 2
  • Qing Tian
    • 2
  • Liping Pang
    • 3
  1. 1.School of ComputerNorth China University of TechnologyBeijingChina
  2. 2.School of Electrical and Information EngineeringNorth China University of TechnologyBeijingChina
  3. 3.School of Aviation Science and EngineeringBeijing University of Aeronautics and Astronautics (BUAA)BeijingChina

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